Big Data Application in Power Systems

Prof. Reza Arghandeh is the Director of Connectivity, Information & Intelligence Lab (Ci2Lab.com) and a Full Professor in Data Science and Machine Learning in the Department of Computer Science, Electrical Engineering, and Mathematical Sciences at the Western Norway University of Applied Sciences (HVL), Bergen, Norway. He is also the HVL Data Science Group (HVL.no/ai). Additionally, he is a Research Professor in the Electrical and Computer Department at Florida State University, USA, where he was an assistant professor from 2015 to 2018. Prior to FSU, he was a postdoctoral scholar at the University of California, Berkeley, EECS Dept 2013-2015. His research interests include data analysis and decision support for smart grids and smart cities. His research has been supported by IBM, the U.S. National Science Foundation, the U.S. Department of Energy, the European Space Agency, the European Commission, and the Research Council of Norway. Yuxun Zhou received his B.S. degree in electrical engineering from Xi’an Jiaotong University, Xi’an, China, in 2009, the Diplome d’Ingénieur degree in applied mathematics from École Centrale Paris, Paris, France, in 2012, and a Ph.D. degree from the Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA, USA, in 2017. He has been an author on over 60 research articles and conference proceedings published in peer-reviewed journals. Dr Zhou’s research interests include statistical learning theory and paradigms for modern information-rich, large-scale, and human-involved systems.

SECTION 1 Harness the Big Data From Power Systems 1. A Holistic Approach to Becoming a Data-Driven Utility 2. Emerging Security and Data Privacy Challenges for Utilities: Case Studies and Solutions 3. The Role of Big Data and Analytics in Utility Innovation 4. Frameworks for Big Data Integration, Warehousing, and Analytics 

SECTION 2 Harness the Power of Big data 5. Moving Toward Agile Machine Learning for Data Analytics in Power Systems 6. Unsupervised Learning Methods for Power System Data Analysis 7. Deep Learning for Power System Data Analysis 8. Compressive Sensing for Power System Data Analysis  9. Time-Series Classification Methods: Review and Applications to Power Systems Data

SECTION 3 Put the Power of Big Data into Power Systems 10. Future Trends for Big Data Application in Power Systems 11. On Data-Driven Approaches for Demand Response 12. Topology Learning in Radial Distribution Grids 13. Grid Topology Identification via Distributed Statistical Hypothesis Testing 14. Supervised Learning-Based Fault Location in Power Grids 15. Data-Driven Voltage Unbalance Analysis in Power Distribution Networks 16. Predictive Analytics for Comprehensive Energy Systems State Estimation 17. Data Analytics for Energy Disaggregation: Methods and Applications 18. Energy Disaggregation and the Utility-Privacy Tradeoff